CN110780670B - Robot obstacle avoidance control method based on fuzzy control algorithm - Google Patents
Robot obstacle avoidance control method based on fuzzy control algorithm Download PDFInfo
- Publication number
- CN110780670B CN110780670B CN201910885384.2A CN201910885384A CN110780670B CN 110780670 B CN110780670 B CN 110780670B CN 201910885384 A CN201910885384 A CN 201910885384A CN 110780670 B CN110780670 B CN 110780670B
- Authority
- CN
- China
- Prior art keywords
- robot
- obstacle
- algorithm
- fuzzy
- obstacle avoidance
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title description 11
- 230000000694 effects Effects 0.000 claims abstract description 16
- 238000004088 simulation Methods 0.000 claims abstract description 13
- 238000011217 control strategy Methods 0.000 claims abstract description 6
- 230000007246 mechanism Effects 0.000 claims description 6
- 238000012360 testing method Methods 0.000 claims description 6
- 230000004888 barrier function Effects 0.000 claims description 4
- 238000011065 in-situ storage Methods 0.000 claims description 4
- 230000002093 peripheral effect Effects 0.000 claims description 2
- 230000007547 defect Effects 0.000 abstract description 4
- 238000005457 optimization Methods 0.000 abstract description 2
- 238000010276 construction Methods 0.000 abstract 1
- 238000001514 detection method Methods 0.000 description 7
- 230000005484 gravity Effects 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 102000014150 Interferons Human genes 0.000 description 1
- 108010050904 Interferons Proteins 0.000 description 1
- 239000000969 carrier Substances 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 239000000306 component Substances 0.000 description 1
- 239000008358 core component Substances 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 229940079322 interferon Drugs 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0255—Control of position or course in two dimensions specially adapted to land vehicles using acoustic signals, e.g. ultra-sonic singals
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0257—Control of position or course in two dimensions specially adapted to land vehicles using a radar
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Abstract
The invention relates to a robot obstacle avoidance control strategy based on a fuzzy control algorithm. Focusing on the construction of the system and the design of an obstacle avoidance control algorithm, the time and workload of design and development are reduced by adopting a graphical programming language, the modules of initialization of the system, optimization of the obstacle avoidance algorithm, speed control and the like are completed, and finally the feasibility and superiority of the algorithm are verified according to simulation. When the environment in front of the robot is complex, the control effect of the conventional VFH algorithm is poor, and the problem can be solved by adopting a mode of mixed control of the VFH algorithm and the fuzzy theory. According to the invention, the fuzzy control algorithm is adopted to compensate the VFH algorithm, and simulation shows that the fuzzy control algorithm can compensate the defects of the VFH algorithm, so that obstacle avoidance is realized under the condition of relatively complex environment.
Description
Technical Field
The invention relates to a motion control method of a mobile robot, which enables the robot to accurately avoid obstacles.
Background
Existing mobile robots rely on conventional VFH algorithms and VFH-derived algorithms, such as VFH +, VFH, etc., to implement basic obstacle avoidance. When the environment is complex, the robot can encounter obstacles with different shapes, and a plurality of difficulties are brought to the robot for avoiding the obstacles. Simulation experiments show that if the mobile robot only depends on the traditional algorithm to realize obstacle avoidance, the robot can touch obstacles or cannot detect the obstacles to different degrees under the following conditions:
(1) When the scanning period of the sensor is long and the moving speed of the robot is high, the robot already touches the obstacle before receiving the obstacle reminding;
(2) When the robot turns, the side surface or the back surface of the robot body may touch an obstacle due to the fact that the robot body is large;
(3) When the area of the obstacle is small and the sensor cannot detect the obstacle, the robot collides with the obstacle in a mode of not recognizing the obstacle, and the situation is dangerous;
(4) If the robot has a backward speed and the back of the robot is not equipped with a sensor, it will hit an obstacle.
Disclosure of Invention
The purpose of the invention is: under the condition that hardware equipment is certain and obstacle avoidance can be realized only by improving a discrimination mechanism, the mobile robot can avoid the obstacle more accurately by improving the discrimination mechanism.
When the application environment is relatively simple and fixed, a set of complete robot obstacle avoidance related algorithm can basically realize the obstacle avoidance effect of the robot; when the application environment is relatively complex, the problem that the obstacle avoidance related algorithm is not enough but not ideal in obstacle avoidance effect can not be fundamentally solved by simulation test or field test assisted by certain parameter change, and in order to achieve the purpose, the technical scheme of the invention provides a robot obstacle avoidance control method based on a fuzzy control algorithm, which is characterized by comprising the following steps of:
step 201, determining mechanism: if the number of the obstacles sensed by the ultrasonic sensor or the laser radar sensor in a certain angle range is less than a preset threshold value, the step 202 is executed, otherwise, the step 203 is executed;
step 202, determining mechanism: obstacle information obtained by an ultrasonic sensor or a laser radar sensor is input into a VFH algorithm, the obstacle information is converted into speed information of the robot by the VFH algorithm, and the robot moves according to the obtained speed information;
step 203, designing an obstacle avoidance algorithm based on the fuzzy control theory: inputting a fuzzy control algorithm into the distances of obstacles collected by an ultrasonic sensor or a laser radar sensor and the rotating angles of the ultrasonic sensor or the laser radar sensor controlled by a servo driver at the same moment, dividing the distances of the obstacles into 5 fuzzy sets of negative big, negative small, zero, positive small and positive big, respectively representing the distances as far, moderate, near and close, dividing the angles into 3 fuzzy sets of negative small, zero and positive small, respectively representing the distances as left front, middle and right front, outputting the moving speed of the robot by the fuzzy control algorithm, wherein the moving speed represents straight movement, in-situ left turning and in-situ right turning;
wherein the fuzzy set of obstacles is defined as { negative large, negative small, zero, positive small, positive large }, specifically { (6 m,3 m), (4m, 3m), (3m, 1m), (1m, 0.8m), (0.8m, 0.32m) }; a fuzzy set of angles between the obstacle and the center point of the robot is defined as { small negative, zero and small positive }, specifically { (45, 0), 0, (0, 45) }; the fuzzy set of the robot speed is defined as { straight line, in-place left turn, in-place right turn }, and is specifically expressed as {6m/s, 2rad/s, -2rad/s };
and 204, reasoning the barrier distance and the robot motion speed output by the fuzzy control algorithm according to a fuzzy reasoning rule so as to control the robot to move, wherein the fuzzy reasoning rule is as follows:
when the robot is close to the obstacle, the robot rotates and moves forwards until the robot is far away from the obstacle;
when the robot is very close to the obstacle and cannot rotate while advancing, the robot immediately stops and rotates in place until no obstacle exists right in front of the robot;
when the robot is far away from the obstacle, the robot moves straight and does not rotate.
Preferably, the membership function of the fuzzy control algorithm selects a triangular membership function and a gaussian function.
preferably, in the fuzzy inference rule, the following is judged by different preset distance thresholds: whether the robot is close to the obstacle, whether the robot is far away from the obstacle, and whether the robot is close to the obstacle.
When the environment in front of the robot is complex, the control effect of the conventional VFH algorithm is deteriorated. According to the invention, the fuzzy control algorithm is adopted to compensate the VFH algorithm, and simulation shows that the fuzzy control algorithm can compensate the defects of the VFH algorithm, so that obstacle avoidance is realized under the condition of relatively complex environment.
In addition, the invention designs a function of counting collision times of the obstacles and displaying graphs. When the robot collides with an obstacle, the position of the collision point is drawn in the graph, and the number of collisions is displayed. Meanwhile, the algorithm is applied to different carriers, so that the superiority of the algorithm can be verified.
Drawings
FIG. 1 is a system flow diagram;
FIG. 2 is a graph of obstacle distance membership;
FIG. 3 is a graph of obstacle angle membership;
FIG. 4 is a velocity membership of the robot;
FIG. 5 is a relationship between obstacle distance, angle and robot speed;
FIG. 6 is a robot trajectory after a basic obstacle avoidance algorithm is employed;
fig. 7 shows a robot trajectory after the present invention is applied.
Detailed Description
The invention will be further illustrated with reference to the following specific examples. It should be understood that these examples are for illustrative purposes only and are not intended to limit the scope of the present invention. Further, it should be understood that various changes or modifications of the present invention may be made by those skilled in the art after reading the teaching of the present invention, and such equivalents may fall within the scope of the present invention as defined in the appended claims.
The invention designs an obstacle avoidance algorithm based on the motion of the robot based on a robot 3D model and a basic module which are provided in simulation software, and the robot controls a motor in real time according to surrounding environment information, adjusts the motion state and achieves the aim of avoiding obstacles of the robot. The application environment of the invention provides various obstacles for the robot, including trees, walls, steep slopes, and the like. In this embodiment, the dc motor controls the motion state of the robot, which is a core component of the system. The robot is 33.8cm wide and 33.2cm long.
Fig. 1 is a block diagram of a robot obstacle avoidance control strategy based on a fuzzy control algorithm, and the work flow of the robot obstacle avoidance control strategy is as follows:
(1) And (5) initializing the system. In this stage, the parts and the simulation system of the robot are mainly initialized, and in addition, the system is required to be configured to store addresses.
(2) The sensors collect data. The system starts to operate after the initialization is completed. The sensor comprises an ultrasonic sensor and a laser radar sensor, wherein the ultrasonic sensor is used for sensing a remote obstacle and providing the moving direction of the robot; the laser radar sensor is used for sensing nearby obstacles and providing control signals for the original rotation of the robot, and the sensor can feed back obstacle information to the robot in real time. The servo driver is a power source of the sensor and controls the ultrasonic sensor to scan within a certain angle range right in front of the robot.
Since the sensor plays an important role in robot motion control, the invention uses two sensors, namely an ultrasonic sensor and a laser radar sensor. The ultrasonic sensor has two functions: first, an obstacle is detected. The ultrasonic sensor scans and detects between the maximum value and the minimum value by taking a certain angle as a minimum unit. The information fed back to the robot body by the ultrasonic sensor comprises the distance between the robot and the obstacle and the angle of the obstacle relative to the center point of the robot position where the sensor is located. Second, the robot is directed. For example, if the central axis of the robot is used as a reference line, the left and right 80 degrees are used as detection ranges, and the minimum unit is 10 degrees, the robot detects obstacle information in 16 directions ahead by using 3m as a detection distance. Conceivably, this arrangement brings about the following disadvantages: when the obstacle is between 2 positions, the robot may not be able to detect and therefore not be able to inform the obstacle, resulting in a collision. In order to solve the problem that the ultrasonic sensor can not completely meet the detection requirement, the invention adds a laser radar sensor which can compensate the defects of the ultrasonic sensor to a certain extent. For example, increase the detection density, increase the detection speed, etc. The invention sets the minimum detection angle of the laser sensor as 1 degree, namely the laser radar sensor can scan all the obstacles in front. Simulation shows that under the condition of reasonable configuration, the ultrasonic sensor and the laser radar sensor can basically meet the requirements of obstacle detection, cannot touch obstacles, successfully complete obstacle avoidance and reach a target position.
Ultrasonic sensor and laser radar sensor are settled on servo motor, because the sensor can rotate along with the motor, the system distributes the different angles of sensor, and the sensor gathers the barrier information of corresponding angle, and last sensor provides the information of barrier for the robot.
(3) And acquiring obstacle information. The system acquires and processes the obstacle information and outputs the processed information to the obstacle avoidance system.
(4) The obstacle avoidance system consists of a VFH algorithm and a fuzzy control algorithm, wherein the VFH algorithm is a main output channel and converts obstacle information into speed information of the robot. When the environment in front of the robot is complex, the control effect of the VFH algorithm is deteriorated. The fuzzy control algorithm is used for compensating the VFH algorithm, and simulation shows that the fuzzy control algorithm can compensate the defects of the VFH algorithm, assist the robot in recognizing obstacles with various shapes under the condition of relatively complex environment, and achieve obstacle avoidance.
In this embodiment, whether the environment ahead is relatively complicated is determined by whether the number of obstacles sensed by the ultrasonic sensor or the lidar sensor is less than a preset threshold. If the number of the obstacles sensed by the ultrasonic sensor or the laser radar sensor in a certain angle range is less than a preset threshold value, the front environment is not complex, the control is performed by adopting a VFH algorithm at the moment, and otherwise, the control is performed by adopting a mixed control of the VFH algorithm and a fuzzy control algorithm.
The fuzzy control mainly designs expert experience and engineering experience into fuzzy rules, then fuzzifies input signals of the system to be used as input of the fuzzy rules, and completes fuzzy reasoning. Finally, the result of fuzzy inference is deblurred as the output quantity of fuzzy control. The fuzzy control is composed of a fuzzification module, a fuzzy reasoning module and a defuzzification module. In the invention, the control mode adopts a multi-input single-output mode. The input quantity of the fuzzification module is the obstacle distance and the obstacle angle acquired by the sensor, the obstacle distance is divided into 5 fuzzy sets, the negative is large, the negative is small, the zero is small, the positive is small and the positive is large, the distances are respectively represented as far distance, moderate distance, near distance and near distance, and a membership function of the fuzzy distance is shown in figure 2; the angles are divided into 3 fuzzy sets with small negative, zero and small positive, which are respectively expressed as front left, middle and front right, and fig. 3 is a membership function thereof. The membership function selects a triangular membership function and a gaussian function. The output quantity is the moving speed of the robot, and the states of the robot can be expressed as straight movement, pivot left turning and pivot right turning. Fig. 2 to 4 are membership functions of the obstacle distance, the obstacle angle and the robot movement speed, respectively. Fig. 5 is a relation between the obstacle distance, the angle and the robot movement speed.
The fuzzy reasoning adopts IF (Interferon) \8230andTHEN (8230), and the specific rules can be summarized as follows:
(1) When the robot is close to the obstacle, the robot rotates and moves forwards until the robot is far away from the obstacle;
(2) When the robot is close to the obstacle and cannot rotate while advancing, the robot immediately stops and rotates in place until no obstacle exists in front of the robot;
(3) When the robot is far away from the obstacle, the robot moves straight and does not rotate.
And reasoning the input and output quantities of the invention according to the reasoning rules.
The fuzzy solving is a process of converting a fuzzy set obtained by fuzzy reasoning into an actual control quantity, and mainly comprises a gravity center method, a maximum membership degree method and a weighted average method. Compared with other two algorithms, the gravity center method has smoother output reasoning control, so the system adopts the gravity center method to solve the ambiguity.
(5) A robot model. The robot model obtains speed information output by the obstacle avoidance system, generates movement, feeds the movement information back to the system, and the system is provided with a sensor to acquire data again.
Certainly, the obstacle information acquired by the sensor can only be used as an aid, and what is most important is how to better apply the obstacle information acquired by the sensor to an obstacle avoidance algorithm. The simulation software has a good man-machine interaction interface, and the operation is more convenient, so the simulation verification is performed on the simulation software. The 3-dimensional model included in the invention comprises a scene model, a robot body, a component model of the robot body and a sensor model. During simulation, the robot can move in a scene at a certain speed, obstacles and surrounding environments in the scene simulate real conditions, and whether the robot can effectively avoid obstacles is verified.
Simulation results show that the conventional VFH algorithm and the fuzzy control algorithm solve the obstacle avoidance tasks which are difficult to complete by the conventional VFH algorithm and the VFH + derived from the VFH, and the obstacle avoidance of the robot can be better realized.
Fig. 6 shows a robot track after a basic obstacle avoidance algorithm is adopted, where a thick line is a track when the robot touches an obstacle, a thin line is a normal track of the robot, and the number of collisions between the robot and the obstacle reaches 132 times. As can be seen from the figure, the number of collisions between the robot and the obstacle is large, and the collisions are large on the parts where the wall or the object is more prominent, because the optimized obstacle avoidance algorithm considers how to avoid the object with the more prominent shape.
As shown in fig. 7, zero collision can be basically realized through the obstacle avoidance algorithm after the fuzzy control optimization, and the obstacle avoidance effect is greatly improved.
Claims (3)
1. A robot obstacle avoidance control strategy based on a fuzzy control algorithm is characterized by comprising the following steps:
step 1, robot and peripheral equipment configuration: the ultrasonic sensor is arranged at the foremost end of the robot, the laser radar sensor is arranged at the central point of the robot, and the central point where the robot is located is set as the origin of a world coordinate system; the robot senses a far obstacle by using the ultrasonic sensor, senses a near obstacle by using the laser radar sensor, and controls the ultrasonic sensor and the laser radar sensor to scan within a certain angle range in front of the robot by using the servo driver;
step 2, in order to solve the problem that the obstacle avoidance effect of the original control algorithm is not ideal, on the basis of the original algorithm, an obstacle avoidance algorithm based on a fuzzy control theory is designed, and the obstacle avoidance effect is optimized, wherein the obstacle avoidance algorithm comprises the following steps:
step 201, determining mechanism: if the number of the obstacles sensed by the ultrasonic sensor or the laser radar sensor in a certain angle range is less than a preset threshold value, the step 202 is executed, otherwise, the step 203 is executed;
step 202, determining a mechanism. Obstacle information obtained by an ultrasonic sensor or a laser radar sensor is input into a VFH algorithm, the obstacle information is converted into speed information of the robot by the VFH algorithm, and the robot moves according to the obtained speed information;
step 203, designing an obstacle avoidance algorithm based on the fuzzy control theory: inputting a fuzzy control algorithm into the distances of obstacles collected by an ultrasonic sensor or a laser radar sensor and the rotating angles of the ultrasonic sensor or the laser radar sensor controlled by a servo driver at the same moment, dividing the distances of the obstacles into 5 fuzzy sets of negative big, negative small, zero, positive small and positive big, respectively representing the distances as far, moderate, near and close, dividing the angles into 3 fuzzy sets of negative small, zero and positive small, respectively representing the distances as left front, middle and right front, outputting the moving speed of the robot by the fuzzy control algorithm, wherein the moving speed represents straight movement, in-situ left turning and in-situ right turning;
wherein, the fuzzy set of the obstacles is defined as { negative big, negative small, zero, positive small, positive big }; a fuzzy set of angles between the obstacle and the center point of the robot is defined as { small negative, zero and small positive }; the fuzzy set of the robot speed is defined as { straight going, in-place left turning, in-place right turning };
and 204, reasoning the barrier distance and the robot motion speed output by the fuzzy control algorithm according to a fuzzy reasoning rule so as to control the robot to move, wherein the fuzzy reasoning rule is as follows:
when the robot is close to the obstacle, the robot rotates and moves forwards until the robot is far away from the obstacle;
when the robot is close to the obstacle and cannot rotate while advancing, the robot immediately stops and rotates in place until no obstacle exists in front of the robot;
when the robot is far away from the obstacle, the robot moves straight and does not rotate.
Step 3, testing: carrying out a large number of effective tests in a simulation environment or on the spot, observing the obstacle avoidance effect of the robot and forming records;
and 4, if important parameters and indexes exist in the obstacle avoidance control algorithm established in the step 2 and influence the obstacle avoidance effect of the robot, changing the important parameters and indexes according to the record provided in the step 3 and improving the obstacle avoidance effect of the obstacle avoidance algorithm established in the step 2.
2. The robot obstacle avoidance control strategy based on the fuzzy control algorithm as claimed in claim 1, wherein the membership function of the fuzzy control algorithm selects a triangular membership function and a gaussian function.
3. The robot obstacle avoidance control strategy based on the fuzzy control algorithm as claimed in claim 1, wherein in the fuzzy inference rule, the judgment is made by different preset distance thresholds: whether the robot is close to the obstacle, whether the robot is far from the obstacle, and whether the robot is close to the obstacle.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910885384.2A CN110780670B (en) | 2019-09-19 | 2019-09-19 | Robot obstacle avoidance control method based on fuzzy control algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910885384.2A CN110780670B (en) | 2019-09-19 | 2019-09-19 | Robot obstacle avoidance control method based on fuzzy control algorithm |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110780670A CN110780670A (en) | 2020-02-11 |
| CN110780670B true CN110780670B (en) | 2023-03-21 |
Family
ID=69383738
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910885384.2A Active CN110780670B (en) | 2019-09-19 | 2019-09-19 | Robot obstacle avoidance control method based on fuzzy control algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110780670B (en) |
Families Citing this family (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111665847A (en) * | 2020-06-24 | 2020-09-15 | 上海应用技术大学 | Dynamic window obstacle avoidance algorithm based on fuzzy logic |
| CN113359707A (en) * | 2021-05-19 | 2021-09-07 | 杭州电子科技大学 | Six-legged robot obstacle avoidance method based on double-layer fuzzy reinforcement learning |
| CN113246137A (en) * | 2021-06-09 | 2021-08-13 | 上海机器人产业技术研究院有限公司 | Robot collision detection method based on external moment estimation model |
| CN115576328B (en) * | 2022-11-15 | 2023-03-28 | 之江实验室 | Robot navigation obstacle avoidance method and device based on fuzzy controller |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103324196A (en) * | 2013-06-17 | 2013-09-25 | 南京邮电大学 | Multi-robot path planning and coordination collision prevention method based on fuzzy logic |
| CN108663681A (en) * | 2018-05-16 | 2018-10-16 | 华南理工大学 | Mobile Robotics Navigation method based on binocular camera Yu two-dimensional laser radar |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8855848B2 (en) * | 2007-06-05 | 2014-10-07 | GM Global Technology Operations LLC | Radar, lidar and camera enhanced methods for vehicle dynamics estimation |
-
2019
- 2019-09-19 CN CN201910885384.2A patent/CN110780670B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103324196A (en) * | 2013-06-17 | 2013-09-25 | 南京邮电大学 | Multi-robot path planning and coordination collision prevention method based on fuzzy logic |
| CN108663681A (en) * | 2018-05-16 | 2018-10-16 | 华南理工大学 | Mobile Robotics Navigation method based on binocular camera Yu two-dimensional laser radar |
Non-Patent Citations (2)
| Title |
|---|
| 基于模糊控制的变电站自动巡检机器人超声波避障的研究与设计;杨惠;《信息通信》;20180815(第08期);第75-77页 * |
| 多传感器信息融合伺服的移动机器人快速跟随;周霞等;《计算机工程与设计》;20130616(第06期);第2247-2251页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110780670A (en) | 2020-02-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110780670B (en) | Robot obstacle avoidance control method based on fuzzy control algorithm | |
| CN109755995B (en) | Robot automatic charging docking method based on ROS robot operating system | |
| CN107562048B (en) | Dynamic obstacle avoidance control method based on laser radar | |
| CN110253570B (en) | Vision-based man-machine safety system of industrial mechanical arm | |
| CN108838991B (en) | Autonomous humanoid double-arm robot and tracking operation system thereof for moving target | |
| Dong et al. | Real-time avoidance strategy of dynamic obstacles via half model-free detection and tracking with 2d lidar for mobile robots | |
| CN112928799B (en) | Automatic butt-joint charging method of mobile robot based on laser measurement | |
| CN112506199B (en) | Local path planning method based on dynamic window method and suitable for Ackerman model robot | |
| CN105425828A (en) | Robot anti-impact double-arm coordination control system based on sensor fusion technology | |
| CN112171675B (en) | Obstacle avoidance method and device for mobile robot, robot and storage medium | |
| Barua et al. | A self-driving car implementation using computer vision for detection and navigation | |
| CN116540731B (en) | Path planning method and system integrating LSTM and SAC algorithms | |
| Yan et al. | Reinforcement learning-based autonomous navigation and obstacle avoidance for USVs under partially observable conditions | |
| CN116576857A (en) | Multi-obstacle prediction navigation obstacle avoidance method based on single-line laser radar | |
| US11741336B2 (en) | Generating and/or using training instances that include previously captured robot vision data and drivability labels | |
| CN111026121A (en) | Multi-level three-dimensional obstacle avoidance control method and device for intelligent sweeper | |
| CN109753071B (en) | Robot welting walking method and system | |
| Jiang et al. | Obstacle detection and tracking for intelligent agricultural machinery | |
| Kim et al. | Curve tracking control for autonomous vehicles with rigidly mounted range sensors | |
| CN112363501A (en) | Obstacle avoidance method, device and system of unmanned sweeping vehicle and storage medium | |
| Nasti et al. | Obstacle avoidance during robot navigation in dynamic environment using fuzzy controller | |
| CN115657664A (en) | Path planning method, system, equipment and medium based on human teaching learning | |
| You et al. | A novel obstacle avoidance method for low-cost household mobile robot | |
| Meng et al. | A navigation framework for mobile robots with 3D LiDAR and monocular camera | |
| Watanabe et al. | Obstacle avoidance for mobile robots using an image-based fuzzy controller |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |